Python Raw.apply_proj方法代码示例

# 需要导入模块: from mne.io import Raw [as 别名]
# 或者: from mne.io.Raw import apply_proj [as 别名]
def test_rank_estimation():
    """Test raw rank estimation
    """
    iter_tests = itt.product([fif_fname, hp_fif_fname], ["norm", dict(mag=1e11, grad=1e9, eeg=1e5)])  # sss
    for fname, scalings in iter_tests:
        raw = Raw(fname)
        (_, picks_meg), (_, picks_eeg) = _picks_by_type(raw.info, meg_combined=True)
        n_meg = len(picks_meg)
        n_eeg = len(picks_eeg)

        raw = Raw(fname, preload=True)
        if "proc_history" not in raw.info:
            expected_rank = n_meg + n_eeg
        else:
            mf = raw.info["proc_history"][0]["max_info"]
            expected_rank = _get_sss_rank(mf) + n_eeg
        assert_array_equal(raw.estimate_rank(scalings=scalings), expected_rank)

        assert_array_equal(raw.estimate_rank(picks=picks_eeg, scalings=scalings), n_eeg)

        raw = Raw(fname, preload=False)
        if "sss" in fname:
            tstart, tstop = 0.0, 30.0
            raw.add_proj(compute_proj_raw(raw))
            raw.apply_proj()
        else:
            tstart, tstop = 10.0, 20.0

        raw.apply_proj()
        n_proj = len(raw.info["projs"])

        assert_array_equal(
            raw.estimate_rank(tstart=tstart, tstop=tstop, scalings=scalings),
            expected_rank - (1 if "sss" in fname else n_proj),
        )

# 需要导入模块: from mne.io import Raw [as 别名]
# 或者: from mne.io.Raw import apply_proj [as 别名]
def test_proj():
    """Test SSP proj operations
    """
    tempdir = _TempDir()
    for proj in [True, False]:
        raw = Raw(fif_fname, preload=False, proj=proj)
        assert_true(all(p['active'] == proj for p in raw.info['projs']))

        data, times = raw[0:2, :]
        data1, times1 = raw[0:2]
        assert_array_equal(data, data1)
        assert_array_equal(times, times1)

        # test adding / deleting proj
        if proj:
            assert_raises(ValueError, raw.add_proj, [],
                          {'remove_existing': True})
            assert_raises(ValueError, raw.del_proj, 0)
        else:
            projs = deepcopy(raw.info['projs'])
            n_proj = len(raw.info['projs'])
            raw.del_proj(0)
            assert_true(len(raw.info['projs']) == n_proj - 1)
            raw.add_proj(projs, remove_existing=False)
            assert_true(len(raw.info['projs']) == 2 * n_proj - 1)
            raw.add_proj(projs, remove_existing=True)
            assert_true(len(raw.info['projs']) == n_proj)

    # test apply_proj() with and without preload
    for preload in [True, False]:
        raw = Raw(fif_fname, preload=preload, proj=False)
        data, times = raw[:, 0:2]
        raw.apply_proj()
        data_proj_1 = np.dot(raw._projector, data)

        # load the file again without proj
        raw = Raw(fif_fname, preload=preload, proj=False)

        # write the file with proj. activated, make sure proj has been applied
        raw.save(op.join(tempdir, 'raw.fif'), proj=True, overwrite=True)
        raw2 = Raw(op.join(tempdir, 'raw.fif'), proj=False)
        data_proj_2, _ = raw2[:, 0:2]
        assert_allclose(data_proj_1, data_proj_2)
        assert_true(all(p['active'] for p in raw2.info['projs']))

        # read orig file with proj. active
        raw2 = Raw(fif_fname, preload=preload, proj=True)
        data_proj_2, _ = raw2[:, 0:2]
        assert_allclose(data_proj_1, data_proj_2)
        assert_true(all(p['active'] for p in raw2.info['projs']))

        # test that apply_proj works
        raw.apply_proj()
        data_proj_2, _ = raw[:, 0:2]
        assert_allclose(data_proj_1, data_proj_2)
        assert_allclose(data_proj_2, np.dot(raw._projector, data_proj_2))

# 需要导入模块: from mne.io import Raw [as 别名]
# 或者: from mne.io.Raw import apply_proj [as 别名]
def test_rank_estimation():
    """Test raw rank estimation
    """
    raw = Raw(fif_fname)
    n_meg = len(pick_types(raw.info, meg=True, eeg=False, exclude='bads'))
    n_eeg = len(pick_types(raw.info, meg=False, eeg=True, exclude='bads'))
    raw = Raw(fif_fname, preload=True)
    assert_array_equal(raw.estimate_rank(), n_meg + n_eeg)
    raw = Raw(fif_fname, preload=False)
    raw.apply_proj()
    n_proj = len(raw.info['projs'])
    assert_array_equal(raw.estimate_rank(tstart=10, tstop=20),
                       n_meg + n_eeg - n_proj)

# 需要导入模块: from mne.io import Raw [as 别名]
# 或者: from mne.io.Raw import apply_proj [as 别名]
def test_proj():
    """Test SSP proj operations
    """
    tempdir = _TempDir()
    for proj in [True, False]:
        raw = Raw(fif_fname, preload=False, proj=proj)
        assert_true(all(p['active'] == proj for p in raw.info['projs']))

        data, times = raw[0:2, :]
        data1, times1 = raw[0:2]
        assert_array_equal(data, data1)
        assert_array_equal(times, times1)

        # test adding / deleting proj
        if proj:
            assert_raises(ValueError, raw.add_proj, [],
                          {'remove_existing': True})
            assert_raises(ValueError, raw.del_proj, 0)
        else:
            projs = deepcopy(raw.info['projs'])
            n_proj = len(raw.info['projs'])
            raw.del_proj(0)
            assert_equal(len(raw.info['projs']), n_proj - 1)
            raw.add_proj(projs, remove_existing=False)
            # Test that already existing projections are not added.
            assert_equal(len(raw.info['projs']), n_proj)
            raw.add_proj(projs[:-1], remove_existing=True)
            assert_equal(len(raw.info['projs']), n_proj - 1)

    # test apply_proj() with and without preload
    for preload in [True, False]:
        raw = Raw(fif_fname, preload=preload, proj=False)
        data, times = raw[:, 0:2]
        raw.apply_proj()
        data_proj_1 = np.dot(raw._projector, data)

        # load the file again without proj
        raw = Raw(fif_fname, preload=preload, proj=False)

        # write the file with proj. activated, make sure proj has been applied
        raw.save(op.join(tempdir, 'raw.fif'), proj=True, overwrite=True)
        raw2 = Raw(op.join(tempdir, 'raw.fif'), proj=False)
        data_proj_2, _ = raw2[:, 0:2]
        assert_allclose(data_proj_1, data_proj_2)
        assert_true(all(p['active'] for p in raw2.info['projs']))

        # read orig file with proj. active
        raw2 = Raw(fif_fname, preload=preload, proj=True)
        data_proj_2, _ = raw2[:, 0:2]
        assert_allclose(data_proj_1, data_proj_2)
        assert_true(all(p['active'] for p in raw2.info['projs']))

        # test that apply_proj works
        raw.apply_proj()
        data_proj_2, _ = raw[:, 0:2]
        assert_allclose(data_proj_1, data_proj_2)
        assert_allclose(data_proj_2, np.dot(raw._projector, data_proj_2))

    tempdir = _TempDir()
    out_fname = op.join(tempdir, 'test_raw.fif')
    raw = read_raw_fif(test_fif_fname, preload=True).crop(0, 0.002, copy=False)
    raw.pick_types(meg=False, eeg=True)
    raw.info['projs'] = [raw.info['projs'][-1]]
    raw._data.fill(0)
    raw._data[-1] = 1.
    raw.save(out_fname)
    raw = read_raw_fif(out_fname, proj=True, preload=False)
    assert_allclose(raw[:, :][0][:1], raw[0, :][0])

# 需要导入模块: from mne.io import Raw [as 别名]
# 或者: from mne.io.Raw import apply_proj [as 别名]
import matplotlib.pyplot as plt
import numpy as np
import mne
from mne.io import Raw
from mne.preprocessing.ica import ICA
from mne.datasets import sample

###############################################################################
# Setup paths and prepare epochs data

data_path = sample.data_path()
raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'

raw = Raw(raw_fname, preload=True)
raw.apply_proj()

picks = mne.pick_types(raw.info, meg=True, eeg=False, eog=True,
                       ecg=True, stim=False, exclude='bads')

tmin, tmax, event_id = -0.2, 0.5, 1
baseline = (None, 0)
reject = None

events = mne.find_events(raw, stim_channel='STI 014')
epochs = mne.Epochs(raw, events, event_id, tmin, tmax, proj=False, picks=picks,
                    baseline=baseline, preload=True, reject=reject)

random_state = np.random.RandomState(42)

###############################################################################